SNIa Cosmology Analysis Results from Simulated LSST Images: From Difference Imaging to Constraints on Dark Energy

B. Sánchez, R. Kessler,D. Scolnic, B. Armstrong,R. Biswas, J. Bogart, J. Chiang, J. Cohen-Tanugi,D. Fouchez,Ph. Gris, K. Heitmann,R. Hložek,S. Jha, H. Kelly, S. Liu, G. Narayan,B. Racine,E. Rykoff,M. Sullivan,C. Walter, M. Wood-Vasey,The LSST Dark Energy Science Collaboration

ASTROPHYSICAL JOURNAL(2022)

引用 10|浏览57
暂无评分
摘要
The Vera Rubin Observatory Legacy Survey of Space and Time (LSST) is expected to process similar to 10(6) transient detections per night. For precision measurements of cosmological parameters and rates, it is critical to understand the detection efficiency, magnitude limits, artifact contamination levels, and biases in the selection and photometry. Here we rigorously test the LSST Difference Image Analysis (DIA) pipeline using simulated images from the Rubin Observatory LSST Dark Energy Science Collaboration Data Challenge (DC2) simulation for the Wide-Fast-Deep survey area. DC2 is the first large-scale (300 deg(2)) image simulation of a transient survey that includes realistic cadence, variable observing conditions, and CCD image artifacts. We analyze similar to 15 deg(2) of DC2 over a 5 yr time span in which artificial point sources from Type Ia supernova (SNIa) light curves have been overlaid onto the images. The magnitude limits per filter are u = 23.66 mag, g = 24.69 mag, r = 24.06 mag, i = 23.45 mag, z = 22.54 mag, and y = 21.62 mag. The artifact contamination levels are similar to 90% of all detections, corresponding to similar to 1000 artifacts deg(-2) in g band, and falling to 300 deg(-2) in y band. The photometry has biases m < 23. Our DIA performance on simulated images is similar to that of the Dark Energy Survey difference-imaging pipeline on real images. We also characterize DC2 image properties to produce catalog-level simulations needed for distance bias corrections. We find good agreement between DC2 data and simulations for distributions of signal-to-noise ratio, redshift, and fitted light-curve properties. Applying a realistic SNIa cosmology analysis for redshifts z < 1, we recover the input cosmology parameters to within statistical uncertainties.
更多
查看译文
AI 理解论文
溯源树
样例
生成溯源树,研究论文发展脉络
Chat Paper
正在生成论文摘要